One common task in surveillance is to determine if someone or something crosses a predetermined boundary or line, e.g. a fence. This type of surveillance is a tedious task if performed manually and therefore various automatic systems has been suggested over the years. Different types of tripwires have been used, from real physical wires to beams of light. In later years the tripwires have entered the logic realm as well. In this incarnation it is implemented in software analysing motion video captured by a camera. Such logic tripwires is generally defining one or a plurality of lines in the captured scene and then the motion video is analysed for identifying objects moving in the video and if an identified object breaks a defined line an event is generated. The event may result in an alarm, an indication on an operator interface, an entry in a log, etc.
In order to have the system generate useful alarms and to avoid generation of false positives the systems generally is configured to only generate an event in the cases when the object crossing the logic line in the scene is of a type that is interesting. In some applications the interesting type of objects is humans, in other applications it is elephants, in yet other applications it is dogs. The interesting types of objects may even be products in a process, e.g. objects transported on a conveyer belt that are not to bounce of the conveyer belt. One problem with a simple tripwire implementation which may result in false positives is that the event is generated as soon as a detected object crosses the line independent of whether the detected object is a mouse, a human, or a truck. This problem has been solved in some known implementations by making the system generate an event only when the detected object is of a particular size or within a particular range of sizes.
In “Cisco Video Analytics User Guide”, 2011, Text Part Number: OL-24428-01, from Cisco Systems Inc., 170 West Tasman Drive, San Jose, Calif. 95134-1706, USA, a system implementing a minimum size filter and a maximum size filter for eliminating objects that are smaller than a specified size and eliminating objects that are larger than a specified size from the general video analytics process. The maximum filter is set in a snapshot of the scene. Two boxes, one for the foreground and one for the background, are presented in the snapshot of the scene. The user is then to change the size of each of the boxes in order to indicate the maximum size of objects in the foreground and in the background, respectively. The same procedure is then performed for the minimum size filter, i.e. boxes are presented and the sizes of the boxes are adjusted. The document further describes two examples in which the maximum size filter and the minimum size filter, respectively, may be used. For example, a shadow from a tree or a tree branch may be mis-classified as a person. This may results in false alarms if the wind blows in the tree and its shadow crosses a video tripwire. In such a case the maximum object filter may be defined to provide the system with enough information to disregard excessively large objects that cross the video tripwire. In another example a small animal such as a squirrel may be misclassified and trigger a false alarm when crossing a video tripwire. This situation may then be solved using a minimum object size filter which makes the system disregard small objects that cross the video tripwire.
Hence, the above implementation solves the problem of false positives resulting from objects having a size distinguishable from the size of the objects that are to be detected. However, the act of setting the filters is quite cumbersome and adjusting the settings for the filters is not easier.